internal bool Reclassify()
{
if (classifying)
{
return(false);
}
classifying = true;
List <Item> positive = new List <Item>();
List <Item> negative = new List <Item>();
foreach (Item item in Items)
{
if (item.State == 1)
{
positive.Add(item);
}
if (item.State == -1)
{
negative.Add(item);
}
}
int count = positive.Count + negative.Count;
if (count == 0)
{
Reset();
classifying = false;
return(false);
}
Vector[] trainData = new Vector[count];
bool[] trainLabels = new bool[count];
Vector[] testData = new Vector[Items.Count - count];
int trainCount = 0, testCount = 0;
foreach (Item item in Items)
{
if (item.State != 0)
{
trainData[trainCount] = item.Data;
trainLabels[trainCount] = item.State == 1;
trainCount++;
}
else
{
testData[testCount++] = item.Data;
}
}
form1.bpm.Train(trainData, trainLabels);
double[] probs = form1.bpm.Test(testData);
testCount = 0;
foreach (Item item in Items)
{
if (item.State == 0)
{
item.probTrue = probs[testCount++];
}
}
classifying = false;
return(true);
}
public static GaussianMixtureColorModel Fit(IEnumerable<Color> pixels, int mixtureComponentCount, double stopTolerance)
{
if (pixels == null)
throw new ArgumentNullException("pixels");
if (mixtureComponentCount < 2)
throw new ArgumentOutOfRangeException("mixtureComponentCount", "Mixture component count should be 2 or more.");
MicrosoftResearch.Infer.Maths.Vector[] observedData = new MicrosoftResearch.Infer.Maths.Vector[pixels.Count()];
int index = 0;
foreach (Color pixel in pixels)
observedData[index++] = pixel.ToInferNetVector();
Mixture<VectorGaussian> result = MixtureUtils.Fit(observedData, mixtureComponentCount, mixtureComponentCount * 5, stopTolerance);
return new GaussianMixtureColorModel(result);
}
internal bool Reclassify()
{
if (classifying) return false;
classifying = true;
List<Item> positive = new List<Item>();
List<Item> negative = new List<Item>();
foreach (Item item in Items)
{
if (item.State == 1) positive.Add(item);
if (item.State == -1) negative.Add(item);
}
int count = positive.Count + negative.Count;
if (count == 0)
{
Reset();
classifying = false;
return false;
}
Vector[] trainData = new Vector[count];
bool[] trainLabels = new bool[count];
Vector[] testData = new Vector[Items.Count - count];
int trainCount = 0, testCount = 0;
foreach (Item item in Items)
{
if (item.State!=0)
{
trainData[trainCount] = item.Data;
trainLabels[trainCount] = item.State==1;
trainCount++;
}
else
{
testData[testCount++] = item.Data;
}
}
form1.bpm.Train(trainData, trainLabels);
double[] probs = form1.bpm.Test(testData);
testCount = 0;
foreach (Item item in Items)
{
if (item.State == 0)
{
item.probTrue = probs[testCount++];
}
}
classifying = false;
return true;
}
public static GaussianMixtureColorModel Fit(IEnumerable <Color> pixels, int mixtureComponentCount, double stopTolerance)
{
if (pixels == null)
{
throw new ArgumentNullException("pixels");
}
if (mixtureComponentCount < 2)
{
throw new ArgumentOutOfRangeException("mixtureComponentCount", "Mixture component count should be 2 or more.");
}
MicrosoftResearch.Infer.Maths.Vector[] observedData = new MicrosoftResearch.Infer.Maths.Vector[pixels.Count()];
int index = 0;
foreach (Color pixel in pixels)
{
observedData[index++] = pixel.ToInferNetVector();
}
Mixture <VectorGaussian> result = MixtureUtils.Fit(observedData, mixtureComponentCount, mixtureComponentCount * 5, stopTolerance);
return(new GaussianMixtureColorModel(result));
}
public Item(string filename, Vector data)
{
this.Filename = filename;
this.Data = data;
}
public Item(string filename, Vector data)
{
this.Filename = filename;
this.Data = data;
}
